Quantifying Geographic Domain Shift to Decouple the Geospatial Transferability of Human Mobility Flow Generations

Abstract

Human mobility serves as an essential proxy for understanding social, economic, and environmental dynamics in urban systems. However, since human mobility data are often scarce due to high collection costs and privacy concerns, generating them from limited mobility observations or auxiliary data sources is necessary. Geospatial transferability, which measures a model’s capability in a new location or unseen region, is a critical dimension for comparing different human mobility generation models. Few studies have studied the intrinsic characteristics of geospatial transferability. To this end, this study systematically investigates the geospatial transferability of four representative human mobility generation models using a large-scale benchmark data set of census-tract-level commuting flows across 2,265 counties in the United States. Inspired by the domain adaptation theory in machine learning, we introduce geographic domain shift to describe the intrinsic differences in geographic feature distributions and spatial structures between source and target regions, which can jointly affect model transferability. Moreover, we propose two metrics, mutual information and spatial shift, to quantify the geographic domain shift. To examine their associations with model transferability, we employ linear mixed-effects regression to analyze the associations between geographic domain shifts and transferability. Our results reveal substantial spatial heterogeneity and asymmetry in transfer performance across regions. Both information shift and spatial shift exhibit statistically significant and complementary explanatory power. This indicates that geospatial transferability depends not only on model design but also on intrinsic geographic differences. These findings provide a novel methodological framework for evaluating and improving the geospatial transferability of human mobility generation models and support more robust and fair human mobility data synthesis across diverse regions. This work also offers insights on spatial transferability for GeoAI model development.